{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:BMQSMMGIROA7NRAMJXN5J5OCTG","short_pith_number":"pith:BMQSMMGI","canonical_record":{"source":{"id":"1810.13105","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-31T04:52:46Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c1da6ed2d7fc35f02f4c7041186c0807eefd6960c958dbeb7ed87d5cb0a69b02","abstract_canon_sha256":"50f504f8443693082670369f1444907b088eb064678db6025a3b218022268c8b"},"schema_version":"1.0"},"canonical_sha256":"0b212630c88b81f6c40c4ddbd4f5c299b34e0d36fbbfb1303be05157e32fd180","source":{"kind":"arxiv","id":"1810.13105","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.13105","created_at":"2026-05-17T23:45:52Z"},{"alias_kind":"arxiv_version","alias_value":"1810.13105v3","created_at":"2026-05-17T23:45:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.13105","created_at":"2026-05-17T23:45:52Z"},{"alias_kind":"pith_short_12","alias_value":"BMQSMMGIROA7","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"BMQSMMGIROA7NRAM","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"BMQSMMGI","created_at":"2026-05-18T12:32:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:BMQSMMGIROA7NRAMJXN5J5OCTG","target":"record","payload":{"canonical_record":{"source":{"id":"1810.13105","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-31T04:52:46Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"c1da6ed2d7fc35f02f4c7041186c0807eefd6960c958dbeb7ed87d5cb0a69b02","abstract_canon_sha256":"50f504f8443693082670369f1444907b088eb064678db6025a3b218022268c8b"},"schema_version":"1.0"},"canonical_sha256":"0b212630c88b81f6c40c4ddbd4f5c299b34e0d36fbbfb1303be05157e32fd180","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:45:52.656311Z","signature_b64":"Zd4VjcOn0cE4ayKiM2TCsX7OeCblrZFDVo+x6ZdXNaQIihApVsiShZfAEd3vGcWqBzxiwWClPILX3mcL2s6qAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0b212630c88b81f6c40c4ddbd4f5c299b34e0d36fbbfb1303be05157e32fd180","last_reissued_at":"2026-05-17T23:45:52.655694Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:45:52.655694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.13105","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:45:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YjQ7V9RyGhJ7AbttPMv283BI3czHoMQGJYUQUjMdNhhCQNeSsjplnGQQM8cML+wLdENLl1FYPHM8DovNdSlDCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T02:26:28.772659Z"},"content_sha256":"206e5ca64cb74f62edfa36b2a995169e3086be87fd92fc9fdb4d57dd4a0840b6","schema_version":"1.0","event_id":"sha256:206e5ca64cb74f62edfa36b2a995169e3086be87fd92fc9fdb4d57dd4a0840b6"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:BMQSMMGIROA7NRAMJXN5J5OCTG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"DBSCAN++: Towards fast and scalable density clustering","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Heinrich Jiang, Jennifer Jang","submitted_at":"2018-10-31T04:52:46Z","abstract_excerpt":"DBSCAN is a classical density-based clustering procedure with tremendous practical relevance. However, DBSCAN implicitly needs to compute the empirical density for each sample point, leading to a quadratic worst-case time complexity, which is too slow on large datasets. We propose DBSCAN++, a simple modification of DBSCAN which only requires computing the densities for a chosen subset of points. We show empirically that, compared to traditional DBSCAN, DBSCAN++ can provide not only competitive performance but also added robustness in the bandwidth hyperparameter while taking a fraction of the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.13105","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:45:52Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"llSl++olLQhxXBq/NHQfUHZsPlGGApB3qBC6+pczv3U++/adeemC6Tpwqqio8IWoJvP42cssaG38T6fweJ43Ag==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-20T02:26:28.773006Z"},"content_sha256":"95c5e5d59fea816a46f884987f0d633a3e81c84e761bb4860d8cdaf1f3729ff9","schema_version":"1.0","event_id":"sha256:95c5e5d59fea816a46f884987f0d633a3e81c84e761bb4860d8cdaf1f3729ff9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BMQSMMGIROA7NRAMJXN5J5OCTG/bundle.json","state_url":"https://pith.science/pith/BMQSMMGIROA7NRAMJXN5J5OCTG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BMQSMMGIROA7NRAMJXN5J5OCTG/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-20T02:26:28Z","links":{"resolver":"https://pith.science/pith/BMQSMMGIROA7NRAMJXN5J5OCTG","bundle":"https://pith.science/pith/BMQSMMGIROA7NRAMJXN5J5OCTG/bundle.json","state":"https://pith.science/pith/BMQSMMGIROA7NRAMJXN5J5OCTG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BMQSMMGIROA7NRAMJXN5J5OCTG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:BMQSMMGIROA7NRAMJXN5J5OCTG","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"50f504f8443693082670369f1444907b088eb064678db6025a3b218022268c8b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-31T04:52:46Z","title_canon_sha256":"c1da6ed2d7fc35f02f4c7041186c0807eefd6960c958dbeb7ed87d5cb0a69b02"},"schema_version":"1.0","source":{"id":"1810.13105","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.13105","created_at":"2026-05-17T23:45:52Z"},{"alias_kind":"arxiv_version","alias_value":"1810.13105v3","created_at":"2026-05-17T23:45:52Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.13105","created_at":"2026-05-17T23:45:52Z"},{"alias_kind":"pith_short_12","alias_value":"BMQSMMGIROA7","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_16","alias_value":"BMQSMMGIROA7NRAM","created_at":"2026-05-18T12:32:16Z"},{"alias_kind":"pith_short_8","alias_value":"BMQSMMGI","created_at":"2026-05-18T12:32:16Z"}],"graph_snapshots":[{"event_id":"sha256:95c5e5d59fea816a46f884987f0d633a3e81c84e761bb4860d8cdaf1f3729ff9","target":"graph","created_at":"2026-05-17T23:45:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"DBSCAN is a classical density-based clustering procedure with tremendous practical relevance. However, DBSCAN implicitly needs to compute the empirical density for each sample point, leading to a quadratic worst-case time complexity, which is too slow on large datasets. We propose DBSCAN++, a simple modification of DBSCAN which only requires computing the densities for a chosen subset of points. We show empirically that, compared to traditional DBSCAN, DBSCAN++ can provide not only competitive performance but also added robustness in the bandwidth hyperparameter while taking a fraction of the ","authors_text":"Heinrich Jiang, Jennifer Jang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-31T04:52:46Z","title":"DBSCAN++: Towards fast and scalable density clustering"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.13105","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:206e5ca64cb74f62edfa36b2a995169e3086be87fd92fc9fdb4d57dd4a0840b6","target":"record","created_at":"2026-05-17T23:45:52Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"50f504f8443693082670369f1444907b088eb064678db6025a3b218022268c8b","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-31T04:52:46Z","title_canon_sha256":"c1da6ed2d7fc35f02f4c7041186c0807eefd6960c958dbeb7ed87d5cb0a69b02"},"schema_version":"1.0","source":{"id":"1810.13105","kind":"arxiv","version":3}},"canonical_sha256":"0b212630c88b81f6c40c4ddbd4f5c299b34e0d36fbbfb1303be05157e32fd180","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0b212630c88b81f6c40c4ddbd4f5c299b34e0d36fbbfb1303be05157e32fd180","first_computed_at":"2026-05-17T23:45:52.655694Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:45:52.655694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Zd4VjcOn0cE4ayKiM2TCsX7OeCblrZFDVo+x6ZdXNaQIihApVsiShZfAEd3vGcWqBzxiwWClPILX3mcL2s6qAA==","signature_status":"signed_v1","signed_at":"2026-05-17T23:45:52.656311Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.13105","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:206e5ca64cb74f62edfa36b2a995169e3086be87fd92fc9fdb4d57dd4a0840b6","sha256:95c5e5d59fea816a46f884987f0d633a3e81c84e761bb4860d8cdaf1f3729ff9"],"state_sha256":"24dbad63dbf7de79055fce25dd52930e2c93fbf4ebe29cf3dc4a7b0874390cc7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pJrSaZvzTSEyRnemlsUZRkJBySE6QtjV5U/paZjj1G1JWebdC0nzHUGxdLKju4auShEFsT/QqfAlCrvQnPx9Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-20T02:26:28.774982Z","bundle_sha256":"eda0621a6b914e606c95bae5991742408f58279e439f23f0a82f43d886cef835"}}